.

library(tidyverse)
library(dplyr)
library(forcats)
library(gmodels)
library(haven)

###Read data

#Put the complete name of the countries. 
Disc <- Disc |> mutate(country = case_when(
  isocntry== "BE" ~ "Belgium",
  isocntry== "DK" ~ "Denmark",
  isocntry== "GR" ~ "Greece",
  isocntry== "ES" ~ "Spain",
  isocntry== "FI" ~"Finland",
  isocntry== "FR" ~ "France",
  isocntry== "IE" ~ "Ireland",
  isocntry== "IT" ~ "Italy",
  isocntry== "LU" ~ "Luxembourg",
  isocntry== "NL" ~ "Netherlands",
  isocntry== "AT" ~ "Austria",
  isocntry== "PT" ~ "Portugal",
  isocntry== "SE" ~ "Sweden",
  isocntry== "DE-W" ~ "Germany (specifically the state of North Rhine-Westphalia)",
  isocntry== "DE-E" ~ "Germany (specifically the state of Berlin)",
  isocntry== "GB" ~ "United Kingdom",
  isocntry== "BG" ~ "Bulgaria",
  isocntry== "CY" ~ "Cyprus",
  isocntry== "CZ" ~ "Czech Republic",
  isocntry== "EE" ~ "Estonia",
  isocntry== "HU" ~ "Hungary",
  isocntry== "LV" ~ "Latvia",
  isocntry== "LT" ~ "Lithuania",
  isocntry== "MT" ~ "Malta",
  isocntry== "PL" ~ "Poland",
  isocntry== "RO" ~ "Romania",
  isocntry== "SK" ~ "Slovakia",
  isocntry== "SI" ~ "Slovenia",
  isocntry== "HR" ~ "Croatia",
  TRUE ~ NA_character_))

view(Disc)

Convert numeric answers from qc19 to character answers

Disc <- Disc %>% mutate(change_docs = case_when(
  qc19 == 1 ~ "Yes",
  qc19 == 2 ~ "No", 
  qc19 == 3 ~ "DK", 
  TRUE ~ NA_character_)) 

Count the qc19 answers by country

library(dplyr)

#sum counts 
opinions_by_country <- Disc %>%
  group_by(country, change_docs) %>%
  summarise(count = n()) %>%
  pivot_wider(names_from = change_docs, values_from = count, values_fill = 0)

opinions_by_country

#percentage counts 
Disc %>%
  group_by(country, change_docs) %>%
  summarise(count = n()) %>%
  ungroup() %>%
  group_by(country) %>%
  mutate(total_count = sum(count)) %>%
  mutate(percentage = (count / total_count) * 100) %>%
  select(country, change_docs, percentage) %>%
  pivot_wider(names_from = change_docs, values_from = percentage, values_fill = 0)
#Top 5 of countries that think that transgender persons should be able to change their civil documents to match their inner gender identity
country_with_most_yes <- opinions_by_country %>%
  filter(Yes > 0) %>%
  arrange(desc(Yes)) %>%
  head(5)
country_with_most_yes
#Top 5 of countries that do not think that transgender persons should be able to change their civil documents to match their inner gender identity
country_with_most_no <- opinions_by_country %>%
  filter(No > 0) %>%
  arrange(desc(No)) %>%
  head(5)
country_with_most_no
#Top 5 of countries that do not think that transgender persons should be able to change their civil documents to match their inner gender identity
country_with_most_DK <- opinions_by_country %>%
  filter(DK > 0) %>%
  arrange(desc(DK)) %>%
  head(5)
country_with_most_DK
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